Linked Life Data

19393128

Source:http://linkedlifedata.com/resource/pubmed/id/19393128

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2009-4-27
pubmed:abstractText
General and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1469-5073
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
91
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
121-32
pubmed:dateRevised
2010-12-28
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations.
pubmed:affiliation
Laboratoire de Génétique Cellulaire (UMR 444), INRA-ENVT, Castanet Tolosan, France.
pubmed:publicationType
Journal Article